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eISSN: 2083-5906
ISSN: 1230-1485
ISSN: 1230-1485
ORIGINAL RESEARCH
Optimizing Tree Species Classification by
Leveraging Median Filtering and Hybrid
Feature Sets from Worldview Satellite Imagery
1
School of Geography and Tourism, Luoyang Normal University, Luoyang, Henan Province, 471934, China
2
School of Resources and Environmental Engineering, Hefei University of Technology, Hefei, Anhui Province, 230009,
China
Submission date: 2026-01-11
Final revision date: 2026-04-14
Acceptance date: 2026-04-23
Online publication date: 2026-09-21
Corresponding author
Huaipeng Liu
Luoyang Normal University, City of Luoyang , Henan Province, China, 471934, luoyang, China
Luoyang Normal University, City of Luoyang , Henan Province, China, 471934, luoyang, China
KEYWORDS
WorldView-2 and WorldView-3median filtertree species identificationperformance evaluationmachine learning
TOPICS
ABSTRACT
The high-precision tree species identification results based on remote sensing are of importance
in forest inventory, management, and biodiversity protection. To evaluate whether the median filtering
technique can effectively improve tree species identification accuracy, this study utilized WorldView-2
and WorldView-3 imagery as data sources and applied different sizes of filter kernels to them.
Subsequently, tree species classification was performed using the random forest (RF) and Extreme
Gradient Boosting (XGBoost) algorithms to explore the extent to which the identification accuracy
could be improved under different filter kernel sizes and whether combining the optimal filtering results
with the original spectral bands could further enhance the accuracy. The results showed that the filter
kernel size of median filtering had a significant impact on tree species identification accuracy, and that
within a certain range, larger filter kernels could yield higher accuracy. In WorldView-3, the accuracy
of tree species identification reached 68.95±0.68% (RF), whereas in WorldView-2, the accuracy reached
82.04±0.30% (RF) after processing with an appropriate filter kernel. When the optimal filtering features
were combined with the original spectral bands, the accuracy was further improved to 68.28±0.94%
in WorldView-3 (RF) and to 85±0.09% in WorldView-2 (XGBoost), representing an improvement of
approximately 7% in WorldView-3 and 8% in WorldView-2 compared with the unfiltered imagery
(61.64±0.35% in WorldView-3 and 77.13±0.15% in WorldView-2). The experimental results demonstrate
that median filtering can effectively improve tree species classification accuracy.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
REFERENCES (43)
1.
APOSTOL B., PETRILA M., LORENT A., CICEU A., GANCZ V., BADEA O. Species discrimination and individual tree detection for predicting main dendrometric characteristics in mixed temperate forests by use of airborne laser scanning and ultra-high-resolution imagery. Science of the Total Environment, 698, 134074, 2020.
2.
CROSS M.D., SCAMBOS T., PACIFICI F., MARSHALL W.E. Determining effective meter-scale image data and spectral vegetation indices for tropical forest tree species differentiation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12 (8), 2934, 2019.
3.
LIU H. Classification of tree species using UAV-based multi-spectral and multi-seasonal images: a multi-featurebased approach. New Forests, 55 (1), 173, 2024.
4.
PU R., LANDRY S. A comparative analysis of high resolution IKONOS and WorldView-2 imagery for mapping urban tree species. Remote Sensing of Environment, 124, 516, 2012.
5.
NAIDOO L., CHO M.A., MATHIEU R., ASNER G.P. Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment. ISPRS Journal of Photogrammetry and Remote Sensing, 69 (3), 167, 2012.
6.
GHOSH A., JOSHI P.K. A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView-2 imagery. International Journal of Applied Earth Observation and Geoinformation, 26 (1), 298, 2014.
7.
FERREIRA M.P., WAGNER F.H., ARAGAO L.E.O.C., SHIMABUKURO Y.E., FILHO C.R.D.S. Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 149, 119, 2019.
8.
HAMRAZ H., JACOBS N.B., CONTRERAS M.A, CLARK C. Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees. ISPRS Journal of Photogrammetry and Remote Sensing, 158, 219, 2019.
9.
SHI Y.F., WANG T.J., SKIDMORE A.K., HEURICH M. Improving LiDAR-based tree species mapping in Central European mixed forests using multi-temporal digital aerial colour-infrared photographs. International Journal of Applied Earth Observation and Geoinformation, 84, 101970, 2020.
10.
ZHANG Z., KAZAKOVA A., MOSKAL L.M., STYERS D.M. Object-based tree species classification in urban ecosystems using LiDAR and hyperspectral data. Forests, 7 (6), 122, 2016.
11.
TORABZADEH H., LEITERER R., HUENI A., SCHAEPMAN M., MORSDORF F. Tree species classification in a temperate mixed forest using a combination of imaging spectroscopy and airborne laser scanning. Agricultural and Forest Meteorology, 279, 107744, 2019.
12.
MODZELEWSKA A., FASSNACHT F.E., STERENCZAK K. Tree species identification within an extensive forest area with diverse management regimes using airborne hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 84, 101960, 2020.
13.
KUREEL N., SARUP J., MATIN S., GOSWAMI S., KUREEL K. Modelling vegetation health and stress using hyperspectral remote sensing data. Modeling Earth Systems and Environment, 8, 733, 2022.
14.
WANG T., ZHANG H., LIN H., FANG C. Texturalspectral feature-based species classification of mangroves in mai po nature reserve from Worldview-3 imagery. Remote Sensing, 8 (1), 24, 2016.
15.
ÅKERBLOM M., RAUMONEN P., MAKIPAA R., KAASALAINEN M. Automatic tree species recognition with quantitative structure models. Remote Sensing of Environment, 191, 1, 2017.
16.
YU X., HYYPPA J., LITKEY P., KAARTINEN H., VASTARANTA M., HOLOPAINEN M. Single-sensor solution to tree species classification using multispectral airborne laser scanning. Remote Sensing, 9 (2), 108, 2017.
17.
LIU H., AN H. Urban greening tree species classification based on HSV colour space of WorldView-2. Journal of the Indian Society of Remote Sensing, 47 (11), 1959, 2019.
18.
LIU H., AN H. Analysis of the importance of five new spectral indices from WorldView-2 in tree species classification. Journal of Spatial Science, 65 (3), 455, 2020.
19.
LIU H. Effectiveness of the spectral area index created by three algorithms for tree species recognition. Annals of Forest Science, 80, 17, 2023.
20.
LIU H. Classification of urban tree species using multifeatures derived from four-season RedEdge-MX data. Computers and Electronics in Agriculture, 194, 106794, 2022.
21.
LIU H. Spectral volume index creation and performance evaluation: a preliminary test for tree species identification. Heliyon, 9 (6), e17203, 2023.
22.
PU R., SHAWN L., YU Q. Assessing the potential of multi-seasonal high resolution pléiades satellite imagery for mapping urban tree species. International Journal of Applied Earth Observation and Geoinformation, 71, 144, 2018.
23.
MASEMOLA C., CHO M.A., RAMOELO A. Assessing the effect of seasonality on leaf and canopy spectra for the discrimination of an alien tree species, acacia mearnsii, from co-occurring native species using parametric and nonparametric classifiers. IEEE Transactions on Geoscience and Remote Sensing, 57 (8), 5853, 2019.
24.
HAN W., ZHANG S., JIAO Q., WU H. Dominant tree species mapping based on multi-temporal CHRIS hyperspectral satellite data. Forest Inventory and Planning, 44 (2), 1, 2019.
25.
IMMITZER M., NEUWIRTH M., BOCK S., BRENNER H., VUOLO F., ATZBERGER C. Optimal input features for tree species classification in central europe based on multi-temporal sentinel-2 data. Remote Sensing, 11 (22), 2599, 2019.
26.
RICHARDS J.A., JIA X. Using suitable neighbors to augment the training set in hyperspectral maximum likelihood classification. IEEE Geoscience and Remote Sensing Letters, 5 (4), 774, 2008.
27.
KEMAL A., SERHAT K., ONUR C. Classification and diagnosis of cervical cancer with stacked autoencoder and softmax classification. Expert Systems with Applications, 115, 557, 2019.
28.
ZHANG B., ZHAO L., ZHANG X. Three-dimensional convolutional neural network model for tree species classification using airborne hyperspectral images. Remote Sensing of Environment, 247, 111938, 2020.
29.
CARVALHO M.D., MARCATO J., MARTINS J.A.C., ZAMBONI P., COSTA C.S., SIQUEIRA H.L., ARAUJO M.S., GONCALVES D.N., FURUYA D.E.G., OSCO L.P., RAMOS A.P.M., LI J., CASTRO A.A.D., GONCALVES W.N. A deep learning-based mobile application for tree species mapping in RGB images. International Journal of Applied Earth Observation and Geoinformation, 114, 103045, 2022.
30.
KWON R., RYU Y., YANG T., ZHONG Z.L., IM J. Merging multiple sensing platforms and deep learning empowers individual tree mapping and species detection at the city scale. ISPRS Journal of Photogrammetry and Remote Sensing, 206, 201, 2023.
31.
PU R. Mapping tree species using advanced remote sensing technologies: a state-of-the-art review and perspective. Journal of Remote Sensing, 2021, 9812624, 2021.
32.
AHMED S., ISLAM S. Methods in detection of median filtering in digital images: a survey. Multimedia Tools and Applications, 82, 43945, 2023.
33.
LIU H., WU J., LI X., LIU Y., SATYBALDIEVNA C.D. Tree species identification using principal component analysis features: an analysis on whether non-vegetation components should be excluded. Applied Ecology and Environmental Research, 23 (1), 1675, 2025.
34.
LIU H., AN H. Extraction of four types of urban ground objects based on a newly created WorldView-2 multicolour spectral index. Journal of the Indian Society of Remote Sensing, 48 (7), 1091, 2020.
35.
LIU H.P., AN H.J., WANG B., ZHANG Q.L. Tree species classification using WorldView-2 images based on recursive texture feature elimination. Journal of Beijing Forestry University, 37 (8), 53, 2015.
36.
VAN DER LINDEN S., RABE A., HELD M., JAKIMOW B., LEITAO P.J., OKUJENI A., SCHWIEDER M., SUESS S., HOSTERT P. The EnMAP-box- A toolbox and application programming interface for EnMAP data processing. Remote Sensing, 7 (9), 11249, 2015.
37.
WU Y., ZHANG X. Object-oriented tree species classification with multi-scale texture features based on airborne hyperspectral images. Journal of Beijing Forestry University, 42 (6), 91, 2020.
38.
SUN M., ZHOU C.G., ZOU C.X., ZHANG Z.N., LIU D. Land type information extraction and landscape pattern dynamic analysis of Laoshan scenic area. Journal of Ecology and Rural Environment, 36 (2), 211, 2020.
39.
WANG X.F., WANG Y., ZHOU C.W., YIN L.C., FENG X.M. Urban forest monitoring based on multiple features at the single tree scale by UAV. Urban Forestry & Urban Greening, 58, 126958, 2021.
40.
TIAN X.M., CHEN L., ZHANG X.L. Classifying tree species in the plantations of southern China based on wavelet analysis and mathematical morphology. Computers and Geosciences, 151, 104757, 2021.
41.
ZHOU X.S., YANG W.N., LUO K., PIAO H.Y., ZHOU T., ZHOU J., TANG X.L. Research on UAV hyperspectral of tree species classification based on machine learning algorithms and spatial resolution adjustment. Remote Sensing Technology and Application, 39 (4), 880, 2024.
42.
HE G., WU T.J. Hyperspectral image classification employing spatial-spectral feature supported by 3D convolution and transformer. Computer Engineering and Applications, 61 (2), 259, 2025.
43.
LIU H.P. Optimal coupling model for tree species identification using derivative calculation and texture filtering. Urban Forestry & Urban Greening, 117, 129296, 2026.
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| eISSN: | 2083-5906 |
| ISSN: | 1230-1485 |
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